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> how much better our programming languages, libraries, APIs, IDEs, linters, etc have become It's just libraries, really. Or libraries are 80% of the gain. So

> how much better our programming languages, libraries, APIs, IDEs, linters, etc have become It's just libraries, really. Or libraries are 80% of the gain. So it's less about automation and tools and more about code reuse (sometimes it's even just binary reuse - you surely do that everyday by calling dynamic libraries). The progress you note in the last 20-30 years is just Internet that became widely available. First at work and Unis, and then at home. We switched from sending disks by mail to downloading stuff from FTP servers. Recently WWW Git front-ends gave it another serious boost. That's what happened. > But demand turns out to be incredibly elastic. A 50% increase in productivity just means that there's 400% more work, because so many new things have now become cost effective to try. I would credit Moore's law for that instead. Really, if one can do e.g. affordable, real-time machine vision stuff today, it has more to do with CPUs and GPUs gaining power every year, than software getting better.

hervature 5y ago
> I would credit Moore's law for that instead. Really, if one can do e.g. affordable, real-time machine vision stuff today, it has more to do with CPUs and GPUs gaining power every year, than software getting better. How can you say that seriously when, for the vast majority of people, a computer vision project consists of `import cv2`?
ramesh31 5y ago
>I would credit Moore's law for that instead. Really, if one can do e.g. affordable, real-time machine vision stuff today, it has more to do with CPUs and GPUs gaining power every year, than software getting better. Not really. AlexNet was a watershed moment for computer science, and the kind of things that were possible by 2015 with AI that weren't possible in 2010 far outstrip the gains in hardware advancement during that time.
- dougabug 5y ago
  True. VGG models halved the ImageNet error rate of AlexNet two years after AlexNet, followed by ResNet models a little over a year later which halved the error rate again. Recent NAS generated models are up to 20x more computationally efficient than ResNet models of the same accuracy.

hervature 5y ago

> I would credit Moore's law for that instead. Really, if one can do e.g. affordable, real-time machine vision stuff today, it has more to do with CPUs and GPUs gaining power every year, than software getting better. How can you say that seriously when, for the vast majority of people, a computer vision project consists of `import cv2`?

ramesh31 5y ago

>I would credit Moore's law for that instead. Really, if one can do e.g. affordable, real-time machine vision stuff today, it has more to do with CPUs and GPUs gaining power every year, than software getting better. Not really. AlexNet was a watershed moment for computer science, and the kind of things that were possible by 2015 with AI that weren't possible in 2010 far outstrip the gains in hardware advancement during that time.

dougabug 5y ago
True. VGG models halved the ImageNet error rate of AlexNet two years after AlexNet, followed by ResNet models a little over a year later which halved the error rate again. Recent NAS generated models are up to 20x more computationally efficient than ResNet models of the same accuracy.